Virtual Labs

Comparison of Linear, Lasso, and Ridge Regression

What is the main advantage of using Lasso Regression over Linear Regression?

a: It reduces training time significantly. Explanation

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b: It automatically selects important features by setting some coefficients to zero. Explanation

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c: It minimizes model bias completely. Explanation

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d: It increases the number of coefficients in the model. Explanation

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How does increasing the alpha value in Ridge Regression affect the model?

a: It increases the magnitude of coefficients. Explanation

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b: It decreases the magnitude of coefficients. Explanation

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c: It has no effect on the model. Explanation

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d: It completely eliminates some coefficients. Explanation

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Which statement correctly describes the role of alpha in Lasso and Ridge Regression?

a: Higher alpha values reduce overfitting by increasing regularization. Explanation

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b: Alpha has no impact on model generalization. Explanation

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c: Lower alpha values decrease model flexibility. Explanation

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d: Alpha controls the learning rate of the model. Explanation

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Which model is most suitable when predictors are highly correlated?

a: Linear Regression Explanation

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b: Lasso Regression Explanation

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c: Ridge Regression Explanation

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d: None of the above Explanation

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Which of the following best describes Linear Regression?

a: A model that always sets some coefficients to zero. Explanation

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b: A model that establishes a linear relationship without any regularization. Explanation

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c: A regularized model that uses L2 penalty. Explanation

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d: A nonlinear model suitable only for classification. Explanation

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Which model is most likely to produce a sparse solution (many coefficients becoming zero)?

a: Linear Regression Explanation

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b: Lasso Regression Explanation

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c: Ridge Regression Explanation

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d: Both Lasso and Ridge Explanation

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